INFOTEC-NLP at SemEval-2026 Task 9: Comparing Regional Transformers and Bag-of-Words Approaches for Polarization Detection in Spanish

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

INFOTEC-NLP participated in Subtask 1 (Spanish) of SemEval-2026 Task 9, addressing the challenging problem of binary polarization classification in short Spanish texts, especially within social media environments. Their evaluation focused on two main strategies: lexical models utilizing Bag-of-Words representations and regionally pre-trained Transformer models specifically for Spanish. The team also investigated a logistic stacking framework designed to combine both lexical and contextual representations. Experimental findings consistently showed that regionally adapted Transformers generally outperformed purely lexical approaches, with the BILMALAT model achieving the strongest performance in this specific task. These results highlight the significant importance of regionally aligned pre-training on social media data for robust polarization detection in Spanish.

Key takeaway

For NLP Engineers developing solutions for Spanish social media analysis, you should prioritize regionally pre-trained Transformer models. These models, like BILMALAT, demonstrate superior performance in polarization detection compared to traditional lexical methods. Integrating a logistic stacking framework that combines both lexical and contextual representations can further enhance accuracy, ensuring your systems effectively capture subtle discursive nuances in regional Spanish.

Key insights

Regionally pre-trained Transformers significantly improve Spanish polarization detection over lexical methods.

Principles

• Regional pre-training is crucial for social media NLP.
• Contextual models surpass purely lexical approaches.

Method

Compared Bag-of-Words and regionally pre-trained Transformers (e.g., BILMALAT) for binary classification, augmented by a logistic stacking framework combining both representation types.

In practice

• Apply regional Transformers for Spanish social media analysis.
• Consider stacking lexical and contextual models.

Topics

• Polarization Detection
• Spanish NLP
• Transformer Models
• Bag-of-Words
• SemEval
• Social Media Analysis
• BILMALAT

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.